






'''simple docstring'''







import unittest

import numpy as np

from transformers import RoFormerConfig, is_flax_available
from transformers.testing_utils import require_flax, slow

from ...test_modeling_flax_common import FlaxModelTesterMixin, ids_tensor, random_attention_mask


if is_flax_available():
  import jax.numpy as jnp

  from transformers.models.roformer.modeling_flax_roformer import (
      FlaxRoFormerForMaskedLM,
      FlaxRoFormerForMultipleChoice,
      FlaxRoFormerForQuestionAnswering,
      FlaxRoFormerForSequenceClassification,
      FlaxRoFormerForTokenClassification,
      FlaxRoFormerModel,
  )




class   __SCREAMING_SNAKE_CASE       (unittest.TestCase       ):


  """simple docstring"""


  def __init__(			self   ,	UpperCamelCase__   ,	UpperCamelCase__=13   ,	UpperCamelCase__=7   ,	UpperCamelCase__=True   ,	UpperCamelCase__=True   ,	UpperCamelCase__=True   ,	UpperCamelCase__=True   ,	UpperCamelCase__=99   ,	UpperCamelCase__=32   ,	UpperCamelCase__=5   ,	UpperCamelCase__=4   ,	UpperCamelCase__=37   ,	UpperCamelCase__="gelu"   ,	UpperCamelCase__=0.1   ,	UpperCamelCase__=0.1   ,	UpperCamelCase__=512   ,	UpperCamelCase__=16   ,	UpperCamelCase__=2   ,	UpperCamelCase__=0.02   ,	UpperCamelCase__=4   ,	):






         """simple docstring"""


         a_						     =			parent
         a_						     =			batch_size
         a_						     =			seq_length
         a_						     =			is_training
         a_						     =			use_attention_mask
         a_						     =			use_token_type_ids
         a_						     =			use_labels
         a_						     =			vocab_size
         a_						     =			hidden_size
         a_						     =			num_hidden_layers
         a_						     =			num_attention_heads
         a_						     =			intermediate_size
         a_						     =			hidden_act
         a_						     =			hidden_dropout_prob
         a_						     =			attention_probs_dropout_prob
         a_						     =			max_position_embeddings
         a_						     =			type_vocab_size
         a_						     =			type_sequence_label_size
         a_						     =			initializer_range
         a_						     =			num_choices







  def    _a       (			self	):






         """simple docstring"""


         a_						     =			ids_tensor([self.batch_size, self.seq_length]   ,	self.vocab_size	)

         a_						     =			None
         if self.use_attention_mask:
                a_						     =			random_attention_mask([self.batch_size, self.seq_length]	)

         a_						     =			None
         if self.use_token_type_ids:
                a_						     =			ids_tensor([self.batch_size, self.seq_length]   ,	self.type_vocab_size	)

         a_						     =			RoFormerConfig(
             vocab_size=self.vocab_size   ,	hidden_size=self.hidden_size   ,	num_hidden_layers=self.num_hidden_layers   ,	num_attention_heads=self.num_attention_heads   ,	intermediate_size=self.intermediate_size   ,	hidden_act=self.hidden_act   ,	hidden_dropout_prob=self.hidden_dropout_prob   ,	attention_probs_dropout_prob=self.attention_probs_dropout_prob   ,	max_position_embeddings=self.max_position_embeddings   ,	type_vocab_size=self.type_vocab_size   ,	is_decoder=UpperCamelCase__   ,	initializer_range=self.initializer_range   ,	)

         return config, input_ids, token_type_ids, attention_mask







  def    _a       (			self	):






         """simple docstring"""


         a_						     =			self.prepare_config_and_inputs()
         a_     ,	a_     ,	a_     ,	a_						     =			config_and_inputs
         a_						     =			{'input_ids': input_ids, 'token_type_ids': token_type_ids, 'attention_mask': attention_mask}
         return config, inputs_dict





@require_flax
class   __SCREAMING_SNAKE_CASE       (__A							,       unittest.TestCase       ):


  """simple docstring"""


  _a  : Optional[int]      			=     True

  _a  : Any      			=     (
      (
          FlaxRoFormerModel,
          FlaxRoFormerForMaskedLM,
          FlaxRoFormerForSequenceClassification,
          FlaxRoFormerForTokenClassification,
          FlaxRoFormerForMultipleChoice,
          FlaxRoFormerForQuestionAnswering,
      )
      if is_flax_available()
      else ()
  )







  def    _a       (			self	):






         """simple docstring"""


         a_						     =			FlaxRoFormerModelTester(self	)







  @slow
  def    _a       (			self	):






         """simple docstring"""


         for model_class_name in self.all_model_classes:
                a_						     =			model_class_name.from_pretrained('junnyu/roformer_chinese_small'   ,	from_pt=UpperCamelCase__	)
                a_						     =			model(np.ones((1, 1)	)	)
                self.assertIsNotNone(UpperCamelCase__	)





@require_flax
class   __SCREAMING_SNAKE_CASE       (unittest.TestCase       ):


  """simple docstring"""


  @slow
  def    _a       (			self	):






         """simple docstring"""


         a_						     =			FlaxRoFormerForMaskedLM.from_pretrained('junnyu/roformer_chinese_base'	)
         a_						     =			jnp.array([[0, 1, 2, 3, 4, 5]]	)
         a_						     =			model(UpperCamelCase__	)[0]

         a_						     =			50_000

         a_						     =			(1, 6, vocab_size)
         self.assertEqual(output.shape   ,	UpperCamelCase__	)

         a_						     =			jnp.array(
             [[[-0.1_205, -1.0_265, 0.2_922], [-1.5_134, 0.1_974, 0.1_519], [-5.0_135, -3.9_003, -0.8_404]]]	)

         self.assertTrue(jnp.allclose(output[:, :3, :3]   ,	UpperCamelCase__   ,	atol=1e-4	)	)












'''simple docstring'''







import json
import logging
import os
import socket

import git
import numpy as np
import torch


logging.basicConfig(
    format="%(asctime)s - %(levelname)s - %(name)s - PID: %(process)d -  %(message)s",
    datefmt="%m/%d/%Y %H:%M:%S",
    level=logging.INFO,
)
__lowerCAmelCase        =							logging.getLogger(__name__)


def       __UpperCamelCase					(      lowercase_       :  str   ):
       """simple docstring"""




       a_						     =			git.Repo(search_parent_directories=lowercase_   )
       a_						     =			{
           'repo_id': str(lowercase_   ),
           'repo_sha': str(repo.head.object.hexsha   ),
           'repo_branch': str(repo.active_branch   ),
       }

       with open(os.path.join(lowercase_   ,       'git_log.json'   )   ,       'w'   ) as f:
              json.dump(lowercase_   ,       lowercase_   ,       indent=4   )


def       __UpperCamelCase					(      lowercase_       :  List[Any]   ):
       """simple docstring"""




       if params.n_gpu <= 0:
              a_						     =			0
              a_						     =			-1
              a_						     =			True
              a_						     =			False
              return

       assert torch.cuda.is_available()

       logger.info('Initializing GPUs'   )
       if params.n_gpu > 1:
              assert params.local_rank != -1

              a_						     =			int(os.environ['WORLD_SIZE']   )
              a_						     =			int(os.environ['N_GPU_NODE']   )
              a_						     =			int(os.environ['RANK']   )

              # number of nodes / node ID
              a_						     =			params.world_size // params.n_gpu_per_node
              a_						     =			params.global_rank // params.n_gpu_per_node
              a_						     =			True

              assert params.n_nodes == int(os.environ['N_NODES']   )
              assert params.node_id == int(os.environ['NODE_RANK']   )

       # local job (single GPU)
       else:
              assert params.local_rank == -1

              a_						     =			1
              a_						     =			0
              a_						     =			0
              a_						     =			0
              a_						     =			1
              a_						     =			1
              a_						     =			False

       # sanity checks
       assert params.n_nodes >= 1
       assert 0 <= params.node_id < params.n_nodes
       assert 0 <= params.local_rank <= params.global_rank < params.world_size
       assert params.world_size == params.n_nodes * params.n_gpu_per_node

       # define whether this is the master process / if we are in multi-node distributed mode
       a_						     =			params.node_id == 0 and params.local_rank == 0
       a_						     =			params.n_nodes > 1

       # summary
       a_						     =			F'--- Global rank: {params.global_rank} - '
       logger.info(PREFIX + 'Number of nodes: %i' % params.n_nodes   )
       logger.info(PREFIX + 'Node ID        : %i' % params.node_id   )
       logger.info(PREFIX + 'Local rank     : %i' % params.local_rank   )
       logger.info(PREFIX + 'World size     : %i' % params.world_size   )
       logger.info(PREFIX + 'GPUs per node  : %i' % params.n_gpu_per_node   )
       logger.info(PREFIX + 'Master         : %s' % str(params.is_master   )   )
       logger.info(PREFIX + 'Multi-node     : %s' % str(params.multi_node   )   )
       logger.info(PREFIX + 'Multi-GPU      : %s' % str(params.multi_gpu   )   )
       logger.info(PREFIX + 'Hostname       : %s' % socket.gethostname()   )

       # set GPU device
       torch.cuda.set_device(params.local_rank   )

       # initialize multi-GPU
       if params.multi_gpu:
              logger.info('Initializing PyTorch distributed'   )
              torch.distributed.init_process_group(
                  init_method='env://'   ,       backend='nccl'   ,       )


def       __UpperCamelCase					(      lowercase_       :  Union[str, Any]   ):
       """simple docstring"""




       np.random.seed(args.seed   )
       torch.manual_seed(args.seed   )
       if args.n_gpu > 0:
              torch.cuda.manual_seed_all(args.seed   )




